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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Dataset associated to the publication NATASTRON-22125961A arXiv:2212.10924

Authors: Bavera, Simone S.;

Dataset associated to the publication NATASTRON-22125961A arXiv:2212.10924

Abstract

# Dataset description 1. Single stellar model grids at Zsun All single stellar model grids are labeled as "single_star_grid_Zsun_*.h5". To access the dataset you should use the POSYDON v1 code and refer to the code documentation, see https://github.com/POSYDON-code/POSYDON/releases/tag/v1.0.0. The data can be accessed, e.g., with POSYDON as ```py from posydon.grids.psygrid import PSyGrid grid = PSyGrid() grid.load(path_to_grid) print(grid.initial_values) print(grid.final_values) print(grid[0].history1['log_L']) print(grid[0].history1['log_Teff']) ``` 2. Binary black hole population at Zsun We provide the binary black hole population in the file labeled "BBH_population_Zsun.h5". This file contains all binary systems forming binary black holes as simulated from a population synthesis model of 50 million binaries generated with POSYDON v1 at solar metallicity. The data can be accessed, e.g., with Pandas as ```py import pandas as pd df = pd.read_hdf('./data_release/source_data/BBH_population_Zsun.h5', key='history') df.head(10) ``` For convenience we also provide a dataset labeled "arXiv_2212.10924.csv.gz" that contains the subset of merging binary black holes obtained from "BBH_population_Zsun.h5" at the time of double compact object formation used to generate the Figure 4 of the main manuscript and compute the binary black hole rates using the software released on https://github.com/ssbvr/BBH_merging_rates. The data can be accessed, e.g., with Pandas as ```py import pandas as pd df = pd.read_csv('./data_release/source_data/arXiv_2212.10924.csv.gz', compression='gzip') df.head(10) ``` Please refer to the following notebook on how use the data to compute the detectable and intrinsic merging population as well as rates, see https://github.com/ssbvr/BBH_merging_rates/blob/main/arXiv_221210924_POSYDON_BBH_Zsun.ipynb

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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